where \(\tau \)  is a temperature hyperparameter to refine the softmax-probability distributions, as was done in [1]}. Specifically, we compute the pseudo cosine similarity matrix \(M^{t\rightarrow \tilde{v}}\)  between the latent representations of text and those of images. The audio-visual link knowledge is transferred from the fixed CLIP to \(M^{a\rightarrow \tilde{v}}_{(i, i)}\) . Then, we match the two types of logits in the sound-image and text-image similarity matrix instead of using hard labels such as 1 for positive pairs. We minimize the following Kullback-Leibler divergence [1]} for the \(i\) -th diagonal component:
\(l_{i}^{KD}= \text{KL}(M^{a\rightarrow \tilde{v}}_{(i, i)}, M^{t\rightarrow \tilde{v}}_{(i, i)}),\) 
